{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "14fe050e",
   "metadata": {},
   "source": [
    "## Data Exploration for CodeDay Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "baf1250d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(style=\"white\")\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "import statsmodels.api as sm\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fd8ec9b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"combinedData_individual.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32604acb",
   "metadata": {},
   "source": [
    "__NOTE:__ gender_pct and ethnicity_pct were calculated using the ENTIRE dataset. This means that the attendees who did not provide their demographics data were included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55cfb7a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eb51b287",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014-11-08 00:00:00\n",
      "2016-11-12 00:00:00\n"
     ]
    }
   ],
   "source": [
    "# Dates of events\n",
    "dates = pd.to_datetime(data[\"startsAt\"])\n",
    "date1 = min(dates)\n",
    "date2 = max(dates)\n",
    "print(date1) # Nov 8, 2014\n",
    "print(date2) # Nov 12, 2016"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3d0688d",
   "metadata": {},
   "source": [
    "### Individual Attendee Demographics\n",
    "\n",
    "The attendees voluntarily provided demographics data, along with their registrations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "33249511",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of attendees: 14057\n",
      "Attendees for whom demographics are known: 7207\n",
      "Attendees for whom demographics are NOT known: 6850\n",
      "Percent of attendees providing demographics data 0.5126982997794693\n"
     ]
    }
   ],
   "source": [
    "# Total number of attendees:\n",
    "print(\"Total number of attendees:\",len(data))\n",
    "\n",
    "# Total number of attendees where gender is known:\n",
    "known_gender = data[data[\"gender\"] != \"unknown\"]\n",
    "\n",
    "# Total number of attendees where gender and ethnicity is known:\n",
    "known_gender_and_ethnicity = known_gender[known_gender[\"ethnicity\"] != \"unknown\"]\n",
    "print(\"Attendees for whom demographics are known:\", len(known_gender_and_ethnicity))\n",
    "\n",
    "# Total number of attendees where gender and ethnicity are NOT known:\n",
    "not_known = data[data[\"gender\"] == \"unknown\"]\n",
    "print(\"Attendees for whom demographics are NOT known:\", len(not_known))\n",
    "\n",
    "# Percent of attendees providing demographics data\n",
    "print(\"Percent of attendees providing demographics data\", len(known_gender_and_ethnicity)/len(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55c9fc17",
   "metadata": {},
   "source": [
    "Removing participants who didn't report their gender leaves us with a dataset of 7207 individuals. When this subset of participants is further filtered for unknown ethnicity, no further individuals are removed, meaning the same individuals who did not report their gender ALSO did not report their ethnicity.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a8e68f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving a file with data ONLY for participants \n",
    "# who provided complete gender and ethnicity data.\n",
    "\n",
    "known_gender_and_ethnicity.to_csv('no_unknowns.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "96bcd19c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for ease of typing will use shorter name\n",
    "no_unknowns = known_gender_and_ethnicity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00ed94c9",
   "metadata": {},
   "source": [
    "Follow-up surveys were sent to all attendees who indicated their interest in coding prior to attending the event was \"low\" or \"none\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d602d927",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of attendees sent follow-up surveys: 4703\n",
      "Total number of follow-up surveys returned: 3190\n",
      "Percent of follow-up surveys returned: 0.6782904529024028\n"
     ]
    }
   ],
   "source": [
    "# Number of attendees with low prior interest in coding:\n",
    "low_interest = data[data[\"codingInterest\"] == \"low\"]\n",
    "\n",
    "# Number of attendees with no prior interest in coding:\n",
    "no_interest = data[data[\"codingInterest\"] == \"none\"]\n",
    "\n",
    "# Total number of attendees sent follow-up (i.e. post-event surveys):\n",
    "print(\"Total number of attendees sent follow-up surveys:\", len(low_interest)+len(no_interest))\n",
    "\n",
    "# Total number of follow-up surveys returned:\n",
    "low_interest_fu = low_interest[low_interest[\"outcome\"] != \"unknown\"]\n",
    "no_interest_fu = no_interest[no_interest[\"outcome\"] != \"unknown\"]\n",
    "print(\"Total number of follow-up surveys returned:\", len(low_interest_fu)+len(no_interest_fu))\n",
    "print(\"Percent of follow-up surveys returned:\", (len(low_interest_fu)+len(no_interest_fu))/(len(low_interest)+len(no_interest)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b6674fb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Low interest unknowns: 0\n",
      "No interest unknowns: 0\n"
     ]
    }
   ],
   "source": [
    "# Checking to see how many attendees with prior 'low' interest in coding \n",
    "# did not report gender and ethnicity.\n",
    "low_interest_unk = low_interest[low_interest['gender'] == 'unknown']\n",
    "print(\"Low interest unknowns:\", len(low_interest_unk))\n",
    "\n",
    "# Checking to see how many attendees with prior 'no' interest in coding \n",
    "# did not report gender and ethnicity.\n",
    "no_interest_unk = no_interest[no_interest['gender'] == 'unknown']\n",
    "print(\"No interest unknowns:\", len(no_interest_unk))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fe00410",
   "metadata": {},
   "outputs": [],
   "source": [
    "not_known.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "37c6844d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "# The attendees who did not provide information on their gender and ethnicity \n",
    "# also did not provide information on their prior level of interest in coding.\n",
    "# Thus, none of these attendees were sent follow-up surveys, \n",
    "# so outcome in unknown by default.\n",
    "print(len(not_known[not_known['gender'] != 'unknown']))\n",
    "print(len(not_known[not_known['ethnicity'] != 'unknown']))\n",
    "print(len(not_known[not_known['codingInterest'] != 'unknown']))\n",
    "print(len(not_known[not_known['outcome'] != 'unknown']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02cccc9d",
   "metadata": {},
   "source": [
    "How many students reporting 'low' or 'no' prior interest in coding presented their work at the event?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "70655326",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of attendees sent follow-up surveys \n",
      " who have info on whether they presented: 3107\n"
     ]
    }
   ],
   "source": [
    "# Number of attendees who responded to f/u survey who have data on whether they presented:\n",
    "low_interest_presented = low_interest_fu[low_interest_fu[\"presented\"] != \"unknown\"]\n",
    "no_interest_presented = no_interest_fu[no_interest_fu[\"presented\"] != \"unknown\"]\n",
    "\n",
    "# Total number:\n",
    "print(\"Total number of attendees sent follow-up surveys \\n who have info on whether they presented:\", len(low_interest_presented)+len(no_interest_presented))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a64e8263",
   "metadata": {},
   "source": [
    "Overall, looking at attendees for whom we have data (i.e. excluding unknowns):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dedc01d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import PercentFormatter\n",
    "from matplotlib.ticker import StrMethodFormatter\n",
    "\n",
    "plotdata = no_unknowns['gender_pct']\n",
    "\n",
    "n, bins, edges = plt.hist(plotdata, weights=np.ones(len(plotdata)) / len(plotdata))\n",
    "\n",
    "plt.xlabel(\"Proportion of participants at my event with my gender\")\n",
    "plt.ylabel(\"Percent of Total Participants\")\n",
    "\n",
    "plt.xticks(bins, rotation = 45)\n",
    "\n",
    "plt.grid(True)\n",
    "\n",
    "plt.gca().xaxis.set_major_formatter(StrMethodFormatter('{x:,.3f}')) # 3 decimal places\n",
    "\n",
    "plt.gca().yaxis.set_major_formatter(PercentFormatter(1))\n",
    "\n",
    "plt.title(\"Participant Gender Similarity at Event\")\n",
    "plt.tight_layout()\n",
    "plt.savefig('gender_sim_hist.png', dpi=300)                        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6e694198",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotdata = no_unknowns['ethnicity_pct']\n",
    "\n",
    "n, bins, edges = plt.hist(plotdata, weights=np.ones(len(plotdata)) / len(plotdata))\n",
    "\n",
    "plt.xlabel(\"Proportion of participants at my event with my ethnicity\")\n",
    "plt.ylabel(\"Percent of Total Participants\")\n",
    "\n",
    "plt.xticks(bins, rotation = 45)\n",
    "\n",
    "plt.grid(True)\n",
    "\n",
    "plt.gca().xaxis.set_major_formatter(StrMethodFormatter('{x:,.3f}')) # 3 decimal places\n",
    "\n",
    "plt.gca().yaxis.set_major_formatter(PercentFormatter(1))\n",
    "\n",
    "plt.title(\"Participant Ethnicity Similarity at Event\")\n",
    "plt.tight_layout()\n",
    "plt.savefig('ethnicity_sim_hist.png', dpi=300) \n",
    "                        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a70e0b9",
   "metadata": {},
   "source": [
    "## Event Demographics\n",
    "\n",
    "We are interested in how attendees of different gender and ethnicity persist with coding activities after attending a CodeDay event. Since part of feeling as though one \"belongs\" to a community is affected by whether or not a person sees others like them in that community, we calculated \"Gender Proportion\" and \"Ethnicity Proportion\" to look at how the overall event demographics matched the demographics of the attendee from the point of view of each attendee."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06527d58",
   "metadata": {},
   "source": [
    "Since we don't know the ethnicity and gender of 6,850 attendees, the percentages for all of these categories represents minimum values, rather than the precise values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ba66503b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['latinx', 'nativeAmerican', 'other', 'black', 'white', 'unknown', 'asian']\n",
      "['nonBinary', 'male', 'female', 'unknown']\n"
     ]
    }
   ],
   "source": [
    "# Construct heatmaps\n",
    "eth = list(set(data[\"ethnicity\"]))\n",
    "print(eth)\n",
    "\n",
    "gen = list(set(data[\"gender\"]))\n",
    "print(gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3437be87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('latinx', 'nonBinary'), ('latinx', 'male'), ('latinx', 'female'), ('latinx', 'unknown'), ('nativeAmerican', 'nonBinary'), ('nativeAmerican', 'male'), ('nativeAmerican', 'female'), ('nativeAmerican', 'unknown'), ('other', 'nonBinary'), ('other', 'male'), ('other', 'female'), ('other', 'unknown'), ('black', 'nonBinary'), ('black', 'male'), ('black', 'female'), ('black', 'unknown'), ('white', 'nonBinary'), ('white', 'male'), ('white', 'female'), ('white', 'unknown'), ('unknown', 'nonBinary'), ('unknown', 'male'), ('unknown', 'female'), ('unknown', 'unknown'), ('asian', 'nonBinary'), ('asian', 'male'), ('asian', 'female'), ('asian', 'unknown')]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{('black', 'nonBinary'): 17,\n",
       " ('white', 'male'): 703,\n",
       " ('other', 'female'): 59,\n",
       " ('white', 'female'): 469,\n",
       " ('latinx', 'female'): 233,\n",
       " ('nativeAmerican', 'male'): 56,\n",
       " ('asian', 'female'): 427,\n",
       " ('latinx', 'male'): 346,\n",
       " ('asian', 'male'): 541,\n",
       " ('black', 'female'): 138,\n",
       " ('white', 'nonBinary'): 37,\n",
       " ('other', 'male'): 96,\n",
       " ('black', 'male'): 241,\n",
       " ('other', 'nonBinary'): 6,\n",
       " ('asian', 'nonBinary'): 34,\n",
       " ('latinx', 'nonBinary'): 10,\n",
       " ('nativeAmerican', 'female'): 37,\n",
       " ('nativeAmerican', 'nonBinary'): 1}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_eth_combinations_LI = []\n",
    "for each_e in eth:\n",
    "    for each_g in gen:\n",
    "        tup = (each_e, each_g)\n",
    "        gen_eth_combinations_LI.append(tup)\n",
    "\n",
    "print(gen_eth_combinations_LI)\n",
    "gen_eth_combi_counts_LI = {}\n",
    "\n",
    "for index in range(len(low_interest)):\n",
    "    curr_ind = low_interest.iloc[index]\n",
    "    curr_ind_eth = curr_ind[\"ethnicity\"]\n",
    "    curr_ind_gen = curr_ind[\"gender\"]\n",
    "    curr_ind_tup = (curr_ind_eth, curr_ind_gen)\n",
    "    if(curr_ind_tup not in gen_eth_combi_counts_LI.keys()):\n",
    "        gen_eth_combi_counts_LI[curr_ind_tup] = 1\n",
    "    else:\n",
    "        gen_eth_combi_counts_LI[curr_ind_tup] += 1\n",
    "    \n",
    "gen_eth_combi_counts_LI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1766109f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ethnicities = ['white', 'asian', 'latinx', 'black','nativeAmerican', 'other']\n",
    "genders = ['male', 'female', 'non-binary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "51813893",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hand transcribed from gen_eth_combi_counts_LI\n",
    "# [white, asian, black, nativeAmerican, other]\n",
    "row1_M_LI = [703, 541, 346, 241, 56, 96]\n",
    "row2_F_LI = [469, 427, 233, 138, 37, 59]\n",
    "row3_NB_LI = [37, 34, 10, 17, 1, 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cbb6e145",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of low-interest participants who are male: 1983 - Percent: 0.5746160533178789\n",
      "\tNumber of low-interest males who continued: 954 - Percent: 0.481089258698941\n",
      "Number of low-interest participants who are female: 1363 - Percent: 0.3949579831932773\n",
      "\tNumber of low-interest females who continued: 629 - Percent: 0.4614820249449743\n",
      "Number of low-interest participants who are non-binary: 105 - Percent: 0.030425963488843813\n",
      "\tNumber of low-interest non-binary ind. who continued: 54 - Percent: 0.5142857142857142\n",
      "\n",
      "Number of no-interest participants who are male: 734 - Percent: 0.5862619808306709\n",
      "\tNumber of low-interest males who continued: 256 - Percent: 0.34877384196185285\n",
      "Number of no-interest participants who are female: 488 - Percent: 0.38977635782747605\n",
      "\tNumber of low-interest females who continued: 156 - Percent: 0.319672131147541\n",
      "Number of no-interest participants who are non-binary: 30 - Percent: 0.023961661341853034\n",
      "\tNumber of low-interest non-binary ind. who continued: 7 - Percent: 0.23333333333333334\n"
     ]
    }
   ],
   "source": [
    "# Gender breakdowns for low- and no-interest groups\n",
    "\n",
    "total_M_LI = low_interest[low_interest[\"gender\"] == \"male\"]\n",
    "total_F_LI = low_interest[low_interest[\"gender\"] == \"female\"]\n",
    "total_NB_LI = low_interest[low_interest[\"gender\"] == \"nonBinary\"]\n",
    "\n",
    "cont_M_LI = total_M_LI[total_M_LI[\"outcome\"] == \"yes\"]\n",
    "cont_F_LI = total_F_LI[total_F_LI[\"outcome\"] == \"yes\"]\n",
    "cont_NB_LI = total_NB_LI[total_NB_LI[\"outcome\"] == \"yes\"]\n",
    "\n",
    "print(\"Number of low-interest participants who are male:\", len(total_M_LI), \"- Percent:\", len(total_M_LI)/len(low_interest))\n",
    "print(\"\\tNumber of low-interest males who continued:\", len(cont_M_LI), \"- Percent:\", len(cont_M_LI)/len(total_M_LI))\n",
    "print(\"Number of low-interest participants who are female:\", len(total_F_LI), \"- Percent:\", len(total_F_LI)/len(low_interest))\n",
    "print(\"\\tNumber of low-interest females who continued:\", len(cont_F_LI), \"- Percent:\", len(cont_F_LI)/len(total_F_LI))\n",
    "print(\"Number of low-interest participants who are non-binary:\", len(total_NB_LI), \"- Percent:\", len(total_NB_LI)/len(low_interest))\n",
    "print(\"\\tNumber of low-interest non-binary ind. who continued:\", len(cont_NB_LI), \"- Percent:\", len(cont_NB_LI)/len(total_NB_LI))\n",
    "\n",
    "print()\n",
    "\n",
    "total_M_NI = no_interest[no_interest[\"gender\"] == \"male\"]\n",
    "total_F_NI = no_interest[no_interest[\"gender\"] == \"female\"]\n",
    "total_NB_NI = no_interest[no_interest[\"gender\"] == \"nonBinary\"]\n",
    "\n",
    "cont_M_NI = total_M_NI[total_M_NI[\"outcome\"] == \"yes\"]\n",
    "cont_F_NI = total_F_NI[total_F_NI[\"outcome\"] == \"yes\"]\n",
    "cont_NB_NI = total_NB_NI[total_NB_NI[\"outcome\"] == \"yes\"]\n",
    "\n",
    "print(\"Number of no-interest participants who are male:\", len(total_M_NI), \"- Percent:\", len(total_M_NI)/len(no_interest))\n",
    "print(\"\\tNumber of low-interest males who continued:\", len(cont_M_NI), \"- Percent:\", len(cont_M_NI)/len(total_M_NI))\n",
    "print(\"Number of no-interest participants who are female:\", len(total_F_NI), \"- Percent:\", len(total_F_NI)/len(no_interest))\n",
    "print(\"\\tNumber of low-interest females who continued:\", len(cont_F_NI), \"- Percent:\", len(cont_F_NI)/len(total_F_NI))\n",
    "print(\"Number of no-interest participants who are non-binary:\", len(total_NB_NI), \"- Percent:\", len(total_NB_NI)/len(no_interest))\n",
    "print(\"\\tNumber of low-interest non-binary ind. who continued:\", len(cont_NB_NI), \"- Percent:\", len(cont_NB_NI)/len(total_NB_NI))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "35bca9ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[703, 541, 346, 241, 56, 96],\n",
       " [469, 427, 233, 138, 37, 59],\n",
       " [37, 34, 10, 17, 1, 6]]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tot_LI = [row1_M_LI, row2_F_LI, row3_NB_LI]\n",
    "tot_LI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "66121508",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_LI = pd.DataFrame(tot_LI, index=genders, columns=ethnicities)\n",
    "total_LI = sum(row1_M_LI) + sum(row2_F_LI) + sum(row3_NB_LI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ca16d2ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "gen_eth_combinations_NI = []\n",
    "for each_e in eth:\n",
    "    for each_g in gen:\n",
    "        tup = (each_e, each_g)\n",
    "        gen_eth_combinations_NI.append(tup)\n",
    "        \n",
    "gen_eth_combi_counts_NI = {}\n",
    "\n",
    "\n",
    "\n",
    "for index in range(len(no_interest)):\n",
    "    curr_ind = no_interest.iloc[index]\n",
    "    curr_ind_eth = curr_ind[\"ethnicity\"]\n",
    "    curr_ind_gen = curr_ind[\"gender\"]\n",
    "    curr_ind_tup = (curr_ind_eth, curr_ind_gen)\n",
    "    if(curr_ind_tup not in gen_eth_combi_counts_NI.keys()):\n",
    "        gen_eth_combi_counts_NI[curr_ind_tup] = 1\n",
    "    else:\n",
    "        gen_eth_combi_counts_NI[curr_ind_tup] += 1\n",
    "    \n",
    "#gen_eth_combi_counts_NI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c79dd85b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hand transcribed: [white, asian, black, nativeAmerican, other]\n",
    "row1_M_NI = [254, 221, 132, 81, 19, 27]\n",
    "row2_F_NI = [191, 132, 67, 67, 12, 19]\n",
    "row3_NB_NI = [13, 6, 4, 5, 1, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "07dbb438",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[254, 221, 132, 81, 19, 27], [191, 132, 67, 67, 12, 19], [13, 6, 4, 5, 1, 1]]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tot_NI = [row1_M_NI, row2_F_NI, row3_NB_NI]\n",
    "tot_NI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c1e84aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_NI = pd.DataFrame(tot_NI, index=genders, columns=ethnicities)\n",
    "total_NI = sum(row1_M_NI) + sum(row2_F_NI) + sum(row3_NB_NI)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d454b8fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('black', 'nonBinary'): 25,\n",
       " ('white', 'male'): 1652,\n",
       " ('asian', 'male'): 1310,\n",
       " ('other', 'female'): 108,\n",
       " ('unknown', 'unknown'): 6850,\n",
       " ('black', 'male'): 390,\n",
       " ('latinx', 'male'): 587,\n",
       " ('white', 'female'): 1101,\n",
       " ('latinx', 'female'): 354,\n",
       " ('nativeAmerican', 'male'): 96,\n",
       " ('asian', 'female'): 923,\n",
       " ('white', 'nonBinary'): 84,\n",
       " ('nativeAmerican', 'female'): 59,\n",
       " ('black', 'female'): 257,\n",
       " ('other', 'male'): 170,\n",
       " ('asian', 'nonBinary'): 63,\n",
       " ('other', 'nonBinary'): 8,\n",
       " ('latinx', 'nonBinary'): 17,\n",
       " ('nativeAmerican', 'nonBinary'): 3}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all participants\n",
    "\n",
    "gen_eth_combinations = []\n",
    "for each_e in eth:\n",
    "    for each_g in gen:\n",
    "        tup = (each_e, each_g)\n",
    "        gen_eth_combinations.append(tup)\n",
    "        \n",
    "gen_eth_combi_counts = {}\n",
    "\n",
    "\n",
    "\n",
    "for index in range(len(data)):\n",
    "    curr_ind = data.iloc[index]\n",
    "    curr_ind_eth = curr_ind[\"ethnicity\"]\n",
    "    curr_ind_gen = curr_ind[\"gender\"]\n",
    "    curr_ind_tup = (curr_ind_eth, curr_ind_gen)\n",
    "    if(curr_ind_tup not in gen_eth_combi_counts.keys()):\n",
    "        gen_eth_combi_counts[curr_ind_tup] = 1\n",
    "    else:\n",
    "        gen_eth_combi_counts[curr_ind_tup] += 1\n",
    "    \n",
    "gen_eth_combi_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cb15e4c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hand transcribed: [white, asian, black, nativeAmerican, other]\n",
    "row1_M_all = [1652, 1310, 587, 390, 96, 170]\n",
    "row2_F_all = [1101, 923, 354, 257, 59, 108]\n",
    "row3_NB_all = [84, 63, 17, 25, 3, 8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4edc340f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1652, 1310, 587, 390, 96, 170],\n",
       " [1101, 923, 354, 257, 59, 108],\n",
       " [84, 63, 17, 25, 3, 8]]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tot_all = [row1_M_all, row2_F_all, row3_NB_all]\n",
    "tot_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "641a9e06",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all = pd.DataFrame(tot_all, index=genders, columns=ethnicities)\n",
    "total_all = sum(row1_M_all) + sum(row2_F_all) + sum(row3_NB_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3c80de13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(3, 1, figsize=(10, 10), sharey=True)\n",
    "#fig.suptitle('Demographics of Participants')\n",
    "\n",
    "# All Percents\n",
    "sns.heatmap((df_all/total_all)*100, ax=axes[0],annot = True,cmap= 'coolwarm',  annot_kws={\"fontsize\":20})\n",
    "axes[0].set_title('Demographics of All Participants Providing Gender & Ethnicity Information (%)')\n",
    "\n",
    "# Low-Interest Percents\n",
    "sns.heatmap((df_LI/total_LI)*100, ax=axes[1], annot = True,cmap= 'coolwarm',annot_kws={\"fontsize\":20})\n",
    "axes[1].set_title('Demographics of Low-Interest Participants (%)')\n",
    "\n",
    "# No-Interest Percents\n",
    "sns.heatmap((df_NI/total_NI)*100, ax=axes[2], annot = True,cmap= 'coolwarm',annot_kws={\"fontsize\":20}, square=False)\n",
    "axes[2].set_title('Demographics of No-Interest Participants (%)')\n",
    "\n",
    "plt.subplots_adjust(hspace = 0.3)\n",
    "plt.savefig('heatplot.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7a18e69f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bbox(x0=0.125, y0=0.5368181818181819, x1=0.9, y1=0.88)\n",
      "Bbox(x0=0.125, y0=0.03681818181818186, x1=0.9, y1=-0.12)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# All vs (low + no interest)\n",
    "fig, axes = plt.subplots(2, 1, figsize=(15, 7), sharey=True)\n",
    "\n",
    "# Adjust label pos\n",
    "pos1 = axes[0].get_position() # get the original position \n",
    "print(pos1)\n",
    "pos2 = [pos1.x0, pos1.y0 - 0.5,  pos1.width, pos1.height - 0.5] \n",
    "axes[0].set_position(pos2) # set a new position\n",
    "print(axes[0].get_position())\n",
    "\n",
    "#fig.suptitle('Demographics of Participants with Low or No Coding Interest')\n",
    "\n",
    "# All Percents\n",
    "sns.heatmap((df_all/total_all)*100, ax=axes[0],annot = True,cmap= 'coolwarm',  annot_kws={\"fontsize\":20}, square=False)\n",
    "axes[0].set_title('Demographics of All Participants Providing Gender & Ethnicity Information (%)')\n",
    "\n",
    "\n",
    "# Low-No-Interest Percents\n",
    "sns.heatmap(((df_LI+df_NI)/(total_LI+total_NI))*100, ax=axes[1], annot = True,cmap= 'coolwarm',annot_kws={\"fontsize\":20}, square=False)\n",
    "axes[1].set_title('Demographics of Participants with Low or No Prior Coding Interest (%)')\n",
    "\n",
    "# No-Interest Percents\n",
    "#sns.heatmap((df_NI/total_NI)*100, ax=axes[2], annot = True,cmap= 'coolwarm',annot_kws={\"fontsize\":20})\n",
    "#axes[2].set_title('Demographics of No-Interest Participants (%)')\n",
    "\n",
    "plt.subplots_adjust(hspace = 0.4)\n",
    "plt.savefig('heatplot.png', dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "43eea476",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# All vs (low + no interest)\n",
    "#fig, axes = plt.subplots(1, 1, figsize=(15, 7), sharey=True)\n",
    "\n",
    "# Adjust label pos\n",
    "#pos1 = axes[0].get_position() # get the original position \n",
    "#print(pos1)\n",
    "#pos2 = [pos1.x0, pos1.y0 - 0.5,  pos1.width, pos1.height - 0.5] \n",
    "#axes[0].set_position(pos2) # set a new position\n",
    "#print(axes[0].get_position())\n",
    "\n",
    "#fig.suptitle('Demographics of Participants with Low or No Coding Interest')\n",
    "\n",
    "# All Percents\n",
    "sns.set(rc = {'figure.figsize':(10,4)})\n",
    "ax = sns.heatmap((df_all/total_all)*100,annot = True,cmap= 'coolwarm',  annot_kws={\"fontsize\":20}, square=False)\n",
    "ax.set_title('Demographics of All Participants Providing Gender & Ethnicity Information (%)')\n",
    "\n",
    "\n",
    "# Low-No-Interest Percents\n",
    "#sns.heatmap(((df_LI+df_NI)/(total_LI+total_NI))*100, ax=axes[1], annot = True,cmap= 'coolwarm',annot_kws={\"fontsize\":20}, square=False)\n",
    "#axes[1].set_title('Demographics of Participants with Low or No Prior Coding Interest (%)')\n",
    "\n",
    "# No-Interest Percents\n",
    "#sns.heatmap((df_NI/total_NI)*100, ax=axes[2], annot = True,cmap= 'coolwarm',annot_kws={\"fontsize\":20})\n",
    "#axes[2].set_title('Demographics of No-Interest Participants (%)')\n",
    "\n",
    "plt.subplots_adjust(hspace = 0.8)\n",
    "plt.savefig('heatplot.png', dpi=600, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da5fe1f8",
   "metadata": {},
   "source": [
    "### Histograms: Ethnicity Proportions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f43b18f",
   "metadata": {},
   "source": [
    "__NOTE:__ Remember that the 'ethnicity_pct' measure includes the attendees for whome gender and ethnicity are 'unknown', so the ethnicity proportions represented in the histograms below are __minimum__ values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "38978206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 492.,  530.,  628.,  919., 1344., 1225., 1100.,  402.,  454.,\n",
       "         113.]),\n",
       " array([0.002849  , 0.03827839, 0.07370777, 0.10913716, 0.14456654,\n",
       "        0.17999593, 0.21542532, 0.2508547 , 0.28628409, 0.32171347,\n",
       "        0.35714286]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(no_unknowns['ethnicity_pct'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "feb7ba54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0028490028490028\n",
      "0.3571428571428571\n"
     ]
    }
   ],
   "source": [
    "print(min(no_unknowns['ethnicity_pct']))\n",
    "print(max(no_unknowns['ethnicity_pct']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0a25063d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 198.,   73.,  674., 1264., 1588., 1576., 1045.,  679.,   74.,\n",
       "          36.]),\n",
       " array([0.00543478, 0.05973001, 0.11402525, 0.16832048, 0.22261571,\n",
       "        0.27691094, 0.33120617, 0.3855014 , 0.43979663, 0.49409187,\n",
       "        0.5483871 ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(no_unknowns['gender_pct'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "91cd208e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0054347826086956\n",
      "0.5483870967741935\n"
     ]
    }
   ],
   "source": [
    "print(min(no_unknowns['gender_pct']))\n",
    "print(max(no_unknowns['gender_pct']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b698ec48",
   "metadata": {},
   "outputs": [],
   "source": [
    "white = data[(data['ethnicity'] == \"white\")]\n",
    "black = data[(data['ethnicity'] == \"black\")]\n",
    "latinx = data[(data['ethnicity'] == \"latinx\")]\n",
    "asian = data[(data['ethnicity'] == \"asian\")]\n",
    "nativeam = data[(data['ethnicity'] == \"nativeAmerican\")]\n",
    "other = data[(data['ethnicity'] == \"other\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b28b972b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(white['ethnicity_pct'], bins=10)  # arguments are passed to np.histogram\n",
    "plt.title(\"Histogram of Ethnicity Proportion for White Attendees\")\n",
    "plt.xlabel(\"Ethnicity Proportion\")\n",
    "plt.ylabel(\"Number of Attendees\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ed2e29ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(mean: 0.22197984630472864 , min: 0.0384615384615384 , max: 0.3571428571428571 )\n"
     ]
    }
   ],
   "source": [
    "print(\"(mean:\", white['ethnicity_pct'].mean(),\", min:\",min(white['ethnicity_pct']),\", max:\",max(white['ethnicity_pct']),\")\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fb59ca1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(asian['ethnicity_pct'], bins=10)  # arguments are passed to np.histogram\n",
    "plt.title(\"Histogram of Ethnicity Proportion for Asian Ethnicity Attendees\")\n",
    "plt.xlabel(\"Ethnicity Proportion\")\n",
    "plt.ylabel(\"Number of Attendees\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "39b209b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "( mean: 0.17841280054620842 , min: 0.048780487804878 , max: 0.3043478260869565 )\n"
     ]
    }
   ],
   "source": [
    "print(\"( mean:\", asian['ethnicity_pct'].mean(),\", min:\",min(asian['ethnicity_pct']),\", max:\",max(asian['ethnicity_pct']),\")\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "33547ab9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(black['ethnicity_pct'], bins=10)  # arguments are passed to np.histogram\n",
    "plt.title(\"Histogram of Ethnicity Proportion for Black Attendees\")\n",
    "plt.xlabel(\"Ethnicity Proportion\")\n",
    "plt.ylabel(\"Number of Attendees\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "78c6d37a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(mean: 0.0853148361933327 , min: 0.0032894736842105 , max: 0.1875 )\n"
     ]
    }
   ],
   "source": [
    "print(\"(mean:\", black['ethnicity_pct'].mean(),\", min:\",min(black['ethnicity_pct']),\", max:\",max(black['ethnicity_pct']),\")\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8792513f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(latinx['ethnicity_pct'], bins=10)  # arguments are passed to np.histogram\n",
    "plt.title(\"Histogram of Ethnicity Proportion for Latinx Attendees\")\n",
    "plt.xlabel(\"Ethnicity Proportion\")\n",
    "plt.ylabel(\"Number of Attendees\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bcd30edf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(mean: 0.11288875840344274 , min: 0.006578947368421 , max: 0.2222222222222222 )\n"
     ]
    }
   ],
   "source": [
    "print(\"(mean:\", latinx['ethnicity_pct'].mean(),\", min:\",min(latinx['ethnicity_pct']),\", max:\",max(latinx['ethnicity_pct']),\")\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "85a12473",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0wzPXyZMn4enpiV69eqFOnTo4evQoNBpNkWtv164dwsPDdUcrNmzYgE8//fSlIFdYz693cdnb22Pnzp0Anl2X4uPjg/j4eL3Lk8lk6N+/PyZNmgQ3N7cCjwi9KgcHB2zZsgUqlQparRZhYWH47LPPdPNzPw2bmJiIU6dOoX379mjXrh3279+vO9q3c+dOVK5cOd8jAvr6pW+bKarXNd6sWbMQFRWFW7duAQCuXLmCKlWq4KuvvoKDg4MufGk0Gsjlcmg0mnx/4ffu3Rvh4eG4cOECunbtCgD47LPPsG/fPiQnJwN4dkTIx8cnz+vS09Oxb98+rFixAkePHsXRo0dx/PhxuLu7Y+3ata+8HlZWVmjevLku7KSnp+s+iFEQe3t77N27Fzk5OdBqtZg6dSr27duHkydPwsfHBx4eHqhatSqio6MLtX9u3rwZSqUSUVFRuvVasWIFDh8+jMTERJibm+cJ/i/+fFm7dq3uvR0xYgQ2btxY4PIcHBwQGRmJ7OxsZGdnY//+/bp5+sarU6cOypUrpwtg9+7dg5ubG65cuYJNmzYhMDAQ7dq1w7fffot27drhxo0br7z+9GbgETAqNRo2bIhu3bqhR48eqFixIsqXL687+tWhQwfMmTMHKpUKQUFBmD59OhQKBVQqFRwcHDB8+HBYWlri+++/x4QJE2BmZoYmTZpALpfr/iJ/3uDBgzF58mSEh4fD3NwcjRs3xvXr14tce8+ePXHv3j306tULWq0WtWvXRmho6Cu9du7cuVi+fHmeaU5OThg5cmSe9S6uyZMnY+rUqVAoFBBCwNfXF02aNMnznM6dO8Pf3x/Tp09Hu3bt4OnpiTlz5ry2T4iNGDECc+bMgYeHB9RqNZo2bYrg4GDd/ISEBHTv3h1ZWVkICgpC3bp1UbduXQwaNAg+Pj7QarWoUqUKVq5cme8FyS1atMDChQvh5+eX55YN+raZonpd41WpUgWzZ8/G0KFDATwLTTt27ICzszMqVKiApk2bokqVKrh16xZq166NDz/8EN26dcPmzZvzjJO7rXft2lUXlB0cHPDll19i8ODBkMlksLKywtKlS/P8QRIREYF69eqhTZs2ecYbMWIEXF1dC7VPhIaGYtq0aVAoFMjJyYGbmxvc3d1fuvXK8/r27Yu7d++ie/fuEEKgVatW8Pb2Rs2aNTF37lz88MMPMDc3R8uWLXH79u1XquPBgwf45ZdfdH9s5LK3t0fz5s2xYcMGDB8+HObm5ujZsye2b9+eZ7ufNGkSZsyYoXtv27Ztq3t/ClqP27dvw83N7aU/DvSNZ2FhgR9++AEzZszA6tWroVarMWbMGHz88cdo1KgRzp49CxcXF1SoUAE1atTIsz2TaZAJQ8faid4QmZmZ+OGHHzBq1ChUqFABV69eha+vL06cOFGoj/vT/9u3bx8iIiKwevVqoy+rY8eOWLRo0UufGiQiMkU8AkYmw8rKChYWFujZsyfkcjnkcjkWLlzI8FVE3t7euH//PpYsWVLSpRARmRweASMiIiKSGC/CJyIiIpIYAxgRERGRxBjAiIiIiCTGAEZEREQksTfyU5CpqZklXUKpVLWqFXujB3tTMPZHP/ZGP/ZGP/amYGWhP2ZmMrzzzlt657+RAUyr5Qc39WFv9GNvCsb+6Mfe6Mfe6MfeFKys94enIImIiIgkxgBGREREJDEGMCIiIiKJMYARERERSYwBjIiIiEhiDGBEREREEmMAIyIiIpLYG3kfMGOyrlQB5cu9uW2xsbE2+JysbDUy0p9KUA0Zg7G20VfZdqTCbZSITN2bmzSMpHw5ORRjd5V0GUa1Z74SGSVdBBUZt1EiojcfT0ESERERSYwBjIiIiEhiDGBEREREEmMAIyIiIpIYAxgRERGRxPgpSDI5+m7TUJpus0BERGUbAxiZHFO/TcOe+cqSLoGIiIqJpyCJiIiIJGbUALZo0SK4uLjA1dUVP//8MwAgOjoaCoUCXbp0wYIFC4y5eCIiIqJSyWinIM+ePYvTp09j9+7dUKvVcHFxgb29PSZOnIgNGzagevXq8PX1RVRUFBwdHY1VBhEREVGpY7QjYK1atcL69eshl8uRmpoKjUaD9PR01K5dG7Vq1YJcLodCocDBgweNVQIRERFRqWTUi/AtLCywePFi/PTTT3B2dkZycjJsbGx0821tbZGUlFTocflptuJjD6m0K03baGmqpbRhb/RjbwpW1vtj9E9Bjh49Gl9++SWGDx+O+Pj4l+bLZLJCj5mSYryv6S0rG4Qxe1jSysp7aOpKyzZqY2Ndamopbdgb/dibgpWF/piZyVC1qpX++cZa8N9//43Y2FgAQIUKFdClSxecOXMG9+/f1z0nOTkZtra2xiqBiIiIqFQyWgBLSEhAUFAQcnJykJOTgyNHjqBv3764efMmbt26BY1Gg71796J9+/bGKoGIiIioVDLaKUhHR0dcvHgRHh4eMDc3R5cuXeDq6ooqVapg1KhRyM7OhqOjI5ydnY1VAhEREVGpZNRrwEaPHo3Ro0fnmWZvb4/du3cbc7FEREREpRrvhE9EREQkMQYwIiIiIokxgBERERFJjAGMiIiISGIMYEREREQSYwAjIiIikhgDGBEREZHEGMCIiIiIJMYARkRERCQxBjAiIiIiiTGAEREREUmMAYyIiIhIYgxgRERERBJjACMiIiKSGAMYERERkcQYwIiIiIgkxgBGREREJDEGMCIiIiKJMYARERERSYwBjIiIiEhi8pIugKSXo9LAxsa6pMsgIiIqsxjAyiBLC3Moxu4q6TKMZs98ZUmXQEREVCCegiQiIiKSmFGPgC1duhQHDhwAADg6OmL8+PEIDAzEuXPnUKFCBQDAyJEj4eTkZMwyiIiIiEoVowWw6OhonDx5EhEREZDJZBg6dCgOHz6MK1euYOPGjbC1tTXWoomIiIhKNYOnIJ8+fYo///wTALBu3ToEBgYiMTHR4MA2NjYICAiApaUlLCwsUK9ePSQmJiIxMRHBwcFQKBRYvHgxtFptsVeCiIiI6E1i8AhYYGAgatWqBTMzM6xfvx4eHh4IDg7GmjVrCnxd/fr1df+Pj4/H/v37sWnTJpw9exYhISGoWLEifH19sWPHDvTu3btQRfMTfESmrzTt56WpltKGvdGPvSlYWe+PwQB2584dLFy4EIsWLYKnpydGjhyJHj16vPICbty4AV9fX0yYMAF169bFsmXLdPO8vb0RGRlZ6ACWkpJRqOcXRlnfIIhKC2Pu54VhY2Ndamopbdgb/dibgpWF/piZyVC1qpX++YYGUKlUAICTJ0+iTZs20Gg0ePLkySst/Ny5cxg0aBDGjh0LT09PXLt2DYcOHdLNF0JALuedMIiIiKhsMRjAWrZsCRcXF2RlZaFly5YYNGgQ2rZta3Dge/fuwc/PD6GhoXB1dQXwLHDNnDkTaWlpUKlU2Lp1Kz8BSURERGWOwcNPwcHBuHDhAuzs7GBmZoYhQ4agffv2Bgdes2YNsrOzMXv2bN20vn37YtiwYejXrx/UajW6dOkCNze34q0BERER0RvGYAAzNzfH/fv3ceLECQwfPhyZmZkwMzN8/9agoCAEBQXlO8/Ly6vwlRIRERGZCINJatWqVdi8eTMOHjyIrKwsLF26NM+F9ERERERUOAYD2L59+/Djjz+iQoUKeOedd7Bt2zbs3btXitqIiIiITJLBACaXy2Fpaal7XKlSJX5ykYiIiKgYDCap6tWr49ixY5DJZMjJycGaNWtQs2ZNKWojIiIiMkmv9CnI8ePH49q1a2jevDmaNWuG+fPnS1EbERERkUkyGMCqVauGdevW4enTp9BoNLCy0n9XVyIiIiIyzOA1YI8fP8Z3332H4cOHQ61WY/LkyXj8+LEUtRERERGZJIMBbPr06ahUqRJSU1NRrlw5ZGZmYvLkyVLURkRERGSSDAaw2NhY+Pv7Qy6Xo0KFCggNDUVsbKwUtRERERGZJIMB7MW73ms0mle6Ez4RERER5c/gRfiffvop5s2bh6ysLJw4cQJhYWFo3bq1FLURERERmSSDh7LGjRuHihUrwtraGgsWLICdnR3Gjx8vRW1EREREJsngETALCwv4+fnBz89PinqIiIiITJ7eAObt7Q2ZTKb3hevXrzdKQURERESmTm8AGzBgAADg8OHDyMzMRI8ePWBubo5du3ahUqVKkhVIREREZGr0BrCuXbsCANasWYMtW7boPvn4+eefo0+fPtJUR0RERGSCDF6E//DhQ2RnZ+seP378GGlpaUYtioiIiMiUGbwI383NDb1794aTkxOEEDh48CB69+4tRW1EREREJslgABszZgwaN26M06dPAwACAgLg6Oho9MKIiIiITJXBAAYAjRo1Qo0aNSCEAABcvXoVjRs3NmphRERERKbKYACbN28eNm7ciKpVq+qmyWQyHDlyxKiFEREREZkqgwHswIED+OWXX1CtWjUp6iEiIiIyeQY/BVm9enWGLyIiIqLXyOARMHt7e8ydOxedOnVC+fLlddNf5RqwpUuX4sCBAwAAR0dHjB8/HtHR0Zg1axays7PRrVs3+Pv7F6N8IiIiojePwQAWHh4OADh48KBu2qtcAxYdHY2TJ08iIiICMpkMQ4cOxd69exEaGooNGzagevXq8PX1RVRUFD9VSURERGWKwQB29OjRIg1sY2ODgIAAWFpaAgDq1auH+Ph41K5dG7Vq1QIAKBQKHDx4kAGMiIiIyhSD14A9fvwYISEh8PHxwaNHjzB58mQ8fvzY4MD169dH8+bNAQDx8fHYv38/ZDIZbGxsdM+xtbVFUlJS0asnIiIiegMZPAI2ffp02NraIjU1FeXKlUNmZiYmT56M+fPnv9ICbty4AV9fX0yYMAFyuRw3b97MM18mkxW6aBsb60K/hojeLKVpP3/dteSoNLC0MH+tY5aU/HpjSutXHKVpGy6Nynp/DAaw2NhYzJo1C1FRUahQoQJCQ0Ph5ub2SoOfO3cOo0ePxsSJE+Hq6oqzZ8/i/v37uvnJycmwtbUtdNEpKRmFfs2rKusbBFFpYcz9vDBsbKxfey02NtZQjN31WscsTfbMV5aa96+kGGO7MSVloT9mZjJUrWqlf77hAfI+RaPRvDQtP/fu3YOfnx9CQ0Ph6uoKAGjWrBlu3ryJW7duQaPRYO/evWjfvr3BsYiIiIhMicEjYJ9++inmzZuHrKwsnDhxAmFhYWjVqpXBgdesWYPs7GzMnj1bN61v376YPXs2Ro0ahezsbDg6OsLZ2bl4a0BERET0hjEYwMaNG4dVq1bB2toaCxYsgIODA/z8/AwOHBQUhKCgoHzn7d69u/CVEhEREZkIgwEsKioKfn5+eUJXZGQkPDw8jFkXERERkcnSG8COHj0KtVqNuXPnQggBIQQAQK1WY8GCBQxgREREREWkN4DFxsbi9OnTSE1Nxfr16///BXI5hgwZIklxRERERKZIbwDLPe0YFhYGLy+vPPPS0tKMXhgRERGRqTJ4P4kdO3a8NK1fv35GKYaIiIioLNB7BMzHxweXL19GdnY2WrZsqZuu1WrRqFEjSYojIiIiMkV6A9iyZcvw6NEjTJw4EbNmzfr/F8jlRbp7PRERERE9o/cUpJWVFf7zn/9g/fr1qFmzpu5fYmIi/P39payRiIiIyKQY/k4hPPv6oX379qF3794YMGAAypUrZ+y6iIiIiExWgTdiTU9Px5YtW7Bp0yY8ffoUGo0G+/fvR+3ataWqj4iIiMjk6D0CNnXqVHTq1Annz59HQEAATpw4gUqVKjF8ERERERWT3iNgERER6Ny5Mzw9PdG2bVuYmZlBJpNJWRsRERGRSdIbwI4dO4adO3fiu+++w9OnT6FQKKBWq6WsjYiIiMgk6T0F+c4772Do0KH45ZdfMGPGDNy8eRMpKSnw9vbG8ePHpayRiIiIyKQY/BSkTCaDo6MjVqxYgV9//RUtWrTAxIkTpaiNiIiIyCS90m0octWoUQPffPMNfvvtN2PVQ0RERGTyChXAcllYWLzuOoiIiIjKDL0B7PDhwwCAnJwcyYohIiIiKgv0BrDFixcDAPr06SNZMURERERlgd7bULz11lvo2rUrkpKSoFAoXpq/Z88eoxZGREREZKr0BrDVq1cjNjYWkyZNQnBwsJQ1EREREZk0vQHMysoKn376KVauXAlbW1tcvXoVarUaTZs2hZWVlZQ1EhEREZmUAr+MGwAyMjLg7e2Nd999FxqNBklJSVixYgVatmwpRX1EREREJsdgAJszZw5CQ0PRpk0bAEBMTAxmz56Nbdu2Gb04IiIiIlNk8D5gmZmZuvAFAPb29nj69OkrLyAzMxNubm5ISEgAAAQGBqJLly5QKpVQKpW6210QERERlRUGj4CZmZnh7t27qFmzJgAgISEB5ubmrzT4xYsXERQUhPj4eN20K1euYOPGjbC1tS1axURERERvOIMBzM/PD3369IG9vT0A4NSpU5gyZcorDb5t2zZMmTIF48ePBwA8efIEiYmJCA4ORmJiIpycnDBy5EiYmRXphvxEREREbySDAaxz586oW7cuTp8+DSEEhg8fjnr16r3S4DNmzMjzODU1FW3atEFISAgqVqwIX19f7NixA7179y5U0TY21oV6PhG9eUrTfl6aanlTsGfsgSFlvT8GAxgA1K1bF3Xr1i32wmrVqoVly5bpHnt7eyMyMrLQASwlJaPYtehT1jcIotLCmPt5YdjYWL/2WsrCz5nS8v6VFGNsN6akLPTHzEyGqlX137ZL0nN/165dw6FDh3SPhRCQy18pAxIRERGZDEkDmBACM2fORFpaGlQqFbZu3QonJycpSyAiIiIqcQYDWO4F9K9Dw4YNMWzYMPTr1w+urq5o1KgR3NzcXtv4RERERG8Cg+f/4uLiIISATCYr8kKOHj2q+7+Xlxe8vLyKPBYRERHRm85gALOxsYGrqyuaNWuGt956Szc9KCjIqIURERERmSqDAaxFixZo0aKFFLUQERERlQkGA9jIkSORlZWFW7duoX79+sjJyUH58uWlqI2IiIjIJBm8CP/ixYvo3LkzfH19kZycDEdHR5w/f16K2oiIiIhMksEANmfOHKxduxaVK1fGe++9h7lz5750h3siIiIienUGA1hWVhY++OAD3WNHR0doNBqjFkVERERkygwGMLlcjrS0NN1tKP755x+jF0VERERkygxehD9ixAgMGDAAKSkp+Oabb3Dq1CmEhIRIURsRERGRSTIYwDp06IC6devi1KlT0Gq1+Oqrr/KckiQiIiKiwnml74JUq9XQarWQy+WwsLAwdk1EREREJs1gANu5cye8vb1x+fJlnDt3Dl5eXjh06JAUtRERERGZJIOnINeuXYvIyEjY2toCABITE+Hr64uuXbsavTgiIiIiU2TwCJiFhYUufAFAjRo1eBqSiIiIqBj0HgG7evUqAMDOzg4hISHo06cPzM3NER4ejpYtW0pWIBEREZGp0RvARo0alefxsWPHdP+XyWQICgoyWlFEREREpkxvADt69KiUdRARERGVGQYvwk9JSUFERAQePXqUZ/r48eONVRMRERGRSTN4Ef6IESNw6dIlCCHy/CMiIiKiojF4BEylUmHp0qVS1EJERERUJhg8Ata4cWNcv35dilqIiIiIygSDR8BatmwJDw8P2NjYQC7//6cfOXLEqIURERERmSqDAWzp0qUIDQ3F+++/L0U9RERERCbPYAB7++234eLiIkUtRERERGWCwWvAPv/8c8yZMwcXLlzA1atXdf9eRWZmJtzc3JCQkAAAiI6OhkKhQJcuXbBgwYLiVU5ERET0hjJ4BGzPnj0AgEOHDummyWQyg9eAXbx4EUFBQYiPjwcAZGVlYeLEidiwYQOqV68OX19fREVFwdHRsRjlExEREb15DAawot4Rf9u2bZgyZYruhq2XLl1C7dq1UatWLQCAQqHAwYMHGcCIiIiozDEYwH7++ed8p3/xxRcFvm7GjBl5HicnJ8PGxkb32NbWFklJSa9SIxEREZFJMRjAnr8HWE5ODs6dO4fWrVsXekH53T1fJpMVehwAsLGxLtLriOjNUZr289JUy5uCPWMPDCnr/TEYwGbNmpXn8YMHD4r0PZDVqlXD/fv3dY+Tk5Nha2tb6HEAICUlo0ivexVlfYMgKi2MuZ8Xho2N9WuvpSz8nCkt719JMcZ2Y0rKQn/MzGSoWtVK//zCDlilShXcvXu30IU0a9YMN2/exK1bt6DRaLB37160b9++0OMQERERvekKdQ2YEAJXrlxB1apVC72gcuXKYfbs2Rg1ahSys7Ph6OgIZ2fnQo9DRERE9KYr1DVgAFC9evVCnYJ8/lOU9vb22L17dyHKIyIiIjI9hb4GjIiIiIiKR28ACwwM1PsimUyGmTNnGqUgIqIclaZUXahemmohItOgN4DVr1//pWkPHz7EunXrULNmTaMWRURlm6WFORRjd5V0GUazZ76ypEsgohKmN4ANHjw4z+Po6GhMmDABCoUCQUFBRi+MiIiIyFQZvAZMrVZj/vz5iIiIwNSpU/nJRSIiIqJiKjCA3bp1C/7+/qhYsSIiIiJQvXp1qeoiIiIiMll6b8S6Y8cO9OrVC05OTti4cSPDFxEREdFrovcIWFBQEMzMzLBq1Sr8+OOPuulCCMhkMpw/f16SAomIiIhMjd4AduTIESnrICIiIioz9AYw3mqCiIiIyDgK/WXcRERERFQ8DGBEREREEmMAIyIiIpIYAxgRERGRxBjAiIiIiCTGAEZEREQkMQYwIiIiIokxgBERERFJjAGMiIiISGIMYEREREQSYwAjIiIikhgDGBEREZHEGMCIiIiIJCYviYUOHDgQqampkMufLT4kJATNmjUriVKIiIiIJCd5ABNC4J9//sGxY8d0AYyIiIioLJH8FOQ///wDmUyGL7/8Eu7u7ti4caPUJRARERGVKMkPQaWnp8Pe3h5Tp05FVlYWBg4ciDp16uCzzz6TuhQiIiKiEiF5AGvRogVatGgBAKhYsSJ69uyJqKioQgUwGxtrY5VHRESvAX9Ov9k9yFFpYGlhbtRllHR/pFjHgkgewP744w+oVCrY29sDeHZNWGGvBUtJyTBGaQBKfoMgIjIFxvw5/SawsbF+o3tgY2MNxdhdJV2GUe2ZrzTqe2RmJkPVqlb65xttyXpkZGRg7ty5yM7ORmZmJiIiIuDk5CR1GUREREQlRvIjYB06dMDFixfh4eEBrVaL/v37605JEhEREZUFJXIfiK+//hpff/11SSyaiIiIqMTxTvhEREREEmMAIyIiIpIYAxgRERGRxBjAiIiIiCTGAEZEREQkMQYwIiIiIokxgBERERFJjAGMiIiISGIlciNWIiKiN5l1pQooX67gX6H8bmEqCAMYERFRIZUvJzfpL6veM19Z0iWYPJ6CJCIiIpIYAxgRERGRxBjAiIiIiCTGAEZEREQkMQYwIiIiIokxgBERERFJjAGMiIiISGIMYEREREQSYwAjIiIikhgDGBEREZHEGMCIiIiIJMYARkRERCQxBjAiIiIiiTGAEREREUmsRALYnj174OLiAicnJ4SFhZVECUREREQlRi71ApOSkrBgwQKEh4fD0tISffv2RevWrfHBBx9IXQoRERFRiZA8gEVHR6NNmzaoXLkyAKBr1644ePAgRo4c+cpjmJnJjFTdM7bvVDDq+KWBqa8j1+/NZ+rraOrrZ+yf06WBqb+Hpr5+gHG3U0Njy4QQwmhLz8fKlSvx5MkT+Pv7AwC2b9+OS5cuYdq0aVKWQURERFRiJL8GLL+8J5OZ/l9KRERERLkkD2DVqlXD/fv3dY+Tk5Nha2srdRlEREREJUbyANa2bVvExMTgwYMHePr0KX755Re0b99e6jKIiIiISozkF+FXq1YN/v7+GDhwIFQqFXr27ImmTZtKXQYRERFRiZH8InwiIiKiso53wiciIiKSGAMYERERkcQYwIiIiIgkxgBGREREJLFSFcAMfUl3bGwsevToga5du2LSpElQq9UAgMTERHh5ecHZ2RkjRozA48ePpS7d6Iram1yLFi3CkiVLpCpXUkXtzblz59CjRw8olUr4+Pjg7t27UpdudEXtzR9//IHu3btDoVBg+PDhSEtLk7p0SRR3v/rrr7/QpEkTqcqVVFF7ExkZiXbt2kGpVEKpVGLBggVSl250Re1NcnIyhg0bBg8PD/Tt2xcJCQlSl250RelNamqqbntRKpXo2LEjWrRoUQLVS0yUEv/++6/o0KGDePjwoXj8+LFQKBTixo0beZ7j6uoqLly4IIQQIjAwUISFhQkhhBg2bJjYu3evEEKIpUuXirlz50pau7EVpzfp6ekiMDBQNG3aVCxevFjq0o2uOL3p0KGDiI2NFUIIsX37djF8+HBJaze24vSmc+fOuufOmzdPzJ8/X9LapVCc/gghxJMnT0SfPn1EgwYNpCxbEsXpTUhIiNizZ4/UJUumOL3x8fERmzZtEkIIsWnTJjFmzBgpSze64u5TQgih0WjEgAEDxO7du6Uqu8SUmiNgz39Jd8WKFXVf0p3r7t27yMrKQvPmzQEA3bt3x8GDB6FSqfD777+ja9eueaabkqL2BgCOHDmC//73v/jiiy9KonSjK2pvcnJyMGbMGDRs2BAAYGdnh3v37pXEKhhNcbab/fv344MPPoBKpUJSUhIqVapUEqtgVMXpDwDMnj0bgwYNkrhqaRSnN5cvX0ZkZCTc3d0xbtw4kzt6WtTePHjwAHFxcejbty8AoEePHvj6669LYA2Mp7j7FADs3LkTFSpUgEKhkLL0ElFqAlhycjJsbGx0j21tbZGUlKR3vo2NDZKSkvDw4UNYWVlBLpfnmW5KitobAPDw8MCwYcNgbm4uXcESKmpvLC0toVQqAQBarRZLly5F586dpStcAsXZbiwsLHDt2jU4OjrizJkzcHV1la5wiRSnP0eOHEFWVhacnZ2lK1hCxemNjY0NRo0ahV27dqF69eoICQmRrnAJFLU3d+7cQY0aNTBz5ky4u7tj9OjRsLCwkLR2YyvOdgMAGo0Gy5cvx9ixY6UpuISVmgAmDHxJt775hl5nCoram7KguL3JycnBuHHjoFar4evra5wiS0hxe2NnZ4fo6Gh89dVX8Pf3N06RJaio/UlJScHy5csRHBxs1PpKUnG2nWXLlqFZs2aQyWQYOnQojh8/brxCS0BRe6NWq/HXX3+hbdu22L17Nzp16oSAgACj1iq14v7MOXHiBOrUqQM7OzvjFFjKlJoAZuhLul+cn5KSAltbW1SpUgWZmZnQaDR5ppuSovamLChObx4/foyhQ4dCrVZj+fLlJvfXaFF7k52djV9//VU33d3dHdeuXZOmaAkVtT/Hjh3Do0eP4OXlpTuKqlQqkZmZKV3xRlbU3mRkZGDt2rW66UII3dkJU1HU3tjY2OCtt95Chw4dAABubm64dOmSdIVLoLi/q3799Ve4uLhIU2wpUGoCmKEv6a5ZsybKlSuHc+fOAXj2SZv27dvDwsICn3zyCfbv359nuikpam/KguL05ttvv0Xt2rWxaNEiWFpalkj9xlTU3sjlcnz33Xe4cuUKAODAgQNo2bJliayDMRW1P7169cKvv/6KXbt2YdeuXQCAXbt2wcrKqkTWwxiK2puKFSti9erVuHjxIgBg48aNcHJyKpF1MJai9ub9999HtWrVEBUVBQD47bff0Lhx4xJZB2Mp7u+qP//8E5988onkdZcYqa/6L8ju3buFq6ur6NKli1i1apUQQoihQ4eKS5cuCSGEiI2NFT169BDOzs7im2++EdnZ2UIIIRISEsSAAQNEt27dxODBg8WjR49KbB2Mpai9ybV48WKT/BSkEEXrzdWrV0WDBg2Ei4uLcHd3F+7u7mLo0KEluRpGUdTt5vfffxeenp7C3d1dfPnll+LevXsltg7GVNz9Sghhkp+CFKJ4246Hh4dwdnYWw4cPF+np6SW2DsZS1N78/fffYsCAAcLV1VX06dNH3Lx5s6RWwWiKs081bdpUZGVllUjdJYFfxk1EREQksVJzCpKIiIiorGAAIyIiIpIYAxgRERGRxBjAiIiIiCTGAEZEREQkMQYwIioSOzs7KBQKKJXKPP8SEhIAAIMHD8aDBw8AAB07dsTly5cLNf6iRYsQGRlZ4HOUSiXS09ORkZGBgQMHFmr8JUuWoE2bNlAqlfDw8IBCocCgQYNw8+bNQo3zumzfvh1hYWEAgM2bN2PVqlUlUgcRScO0blFMRJJat24dqlSpku+8U6dOFWvsMWPGGHxO7o1QExISCh3wAMDFxQWTJ0/WPd6wYQPGjh2L8PDwQo9VXOfOnUP9+vUBAP369ZN8+UQkLQYwInrtAgMDAQA+Pj66Izlbt27FlClT8ODBAyiVSvj7++PMmTNYsGABatWqhRs3biAnJweTJ09GmzZtEBAQgPr162PIkCG4ePEipk+fjqdPn8LCwgLjx4+Hvb097OzsEBMTg8DAQGRlZUGpVGLIkCHYtGkTtmzZAgBITExE7969cfToUYPfeGBvb4/vv/8eAODt7Y23334b//zzD/r16wcnJydMnToVd+/ehRACHh4eGDp0KBISEuDt7Y1WrVohLi4OQghMnjwZn3zyCVQqFWbPno2YmBiYm5ujadOmCAwMhJWVFTp27IimTZvi2rVr+Oabb3D06FGcOnUK5cuXx4MHD/Dw4UNMnjwZN27cQEhICB49egSZTIbBgwfDw8OjwN4RUenHU5BEVGQ+Pj55Tj/6+fkBAGbNmgXg2RGy6tWrAwDKlSuH8PBwbN++HT/99BPu3bsHALh06RIGDx6MyMhI9OzZE0uXLs2zDJVKBT8/P/j5+WHv3r2YNm0aZs6cCa1Wq3vOrFmzUL58eezatQvOzs64ffs2/ve//wF4dmrP09PTYPhSq9XYsWMHWrdurZtWqVIl7N+/H97e3hg3bhxat26NPXv2YPPmzdi9ezf27dsH4FnIa9euHXbt2oWxY8fi66+/hkqlwvLly5GcnKz72iKtVou5c+fqxq9fvz4OHDgAJycndOzYEYMGDYKXl1eemkaMGAFvb2/s2bMHP/74I77//ntcuHDhlXpHRKUXj4ARUZEVdAryRW5ubgAAGxsbvPvuu0hNTQUA1KhRA40aNQIAfPjhh4iIiMjzuuvXr8PMzAyff/45AKBJkybYs2eP3uVYWlqiV69e2LZtGyZMmICIiAhs3Lgx3+fu379f9710KpUKjRs3xrRp03Tzc7+X7smTJzh//jx++uknAIC1tTW6d++O48ePo1mzZnj77behUCgAAI6OjjA3N8e1a9dw/Phx+Pv7677o3dvbWxdSnx9fn/j4eGRnZ6NLly4Ann2ZcZcuXXDixAm0bt3aYO+IqPRiACMiScjl///jRiaTIfdb0MqXL5/v9Fzm5uaQyWR5pl2/fh1169bVu6w+ffqgV69eaNWqFerXr4///Oc/+T7vxWvAXlSxYkUAgFarfakurVYLtVqtq/HFeebm5nmO0uVOV6lUL42vz4uvBwAhhG65hnpHRKUXT0ESkVGYm5vrgkJx1K1bFzKZTHdR/9WrV+Hj45MnnMjlcmg0Gl0AqVGjBpo3b46ZM2e+lgvarays0KxZM92nFDMyMhAZGYm2bdsCAB48eIDjx48DAI4ePQoLCws0aNAADg4O2LJlC1QqFbRaLcLCwvDZZ5/lu4z8+lWnTh1YWFjgl19+AQAkJSXh0KFDuuUS0ZuLAYyIiuzFa8CUSiWioqIAAE5OTujfvz+uX79erGVYWlpiyZIlWLp0KZRKJaZMmYIlS5bkuabLxsYGH374Ibp164aHDx8CALp37w6tVgtHR8diLT9XaGgoYmJioFAo0LNnT3Tp0gXdu3cH8Oz6tl27dsHd3R0rVqzAsmXLYG5ujhEjRuDdd9+Fh4cHunXrBrVajUmTJuU7fvv27bFhwwasXLlSN83CwgI//PAD1q9fD4VCgS+++AJ+fn680J7IBMgEj1kTkYnRarUICQlBjRo1MGzYMKMuKyEhAQqFQndhPBHRq+ARMCIyKZmZmWjdujXu3LmDAQMGlHQ5RET54hEwIiIiIonxCBgRERGRxBjAiIiIiCTGAEZEREQkMQYwIiIiIokxgBERERFJjAGMiIiISGL/BxpK/sPNMLIbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(nativeam['ethnicity_pct'], bins=\"auto\")  # arguments are passed to np.histogram\n",
    "plt.title(\"Histogram of Ethnicity Proportion for Native American Attendees\")\n",
    "plt.xlabel(\"Ethnicity Proportion\")\n",
    "plt.ylabel(\"Number of Attendees\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f3dd3d48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(mean: 0.026371237645922956 , min: 0.0034364261168384 , max: 0.0746268656716417 )\n"
     ]
    }
   ],
   "source": [
    "print(\"(mean:\", nativeam['ethnicity_pct'].mean(),\", min:\",min(nativeam['ethnicity_pct']),\", max:\",max(nativeam['ethnicity_pct']),\")\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c39377ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(other['ethnicity_pct'], bins=\"auto\")  # arguments are passed to np.histogram\n",
    "plt.title(\"Histogram of Ethnicity Proportion for 'Other' Attendees\")\n",
    "plt.xlabel(\"Ethnicity Proportion\")\n",
    "plt.ylabel(\"Number of Attendees\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d946c8d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(mean: 0.034464735358817285 , min: 0.0028490028490028 , max: 0.0793650793650793 )\n"
     ]
    }
   ],
   "source": [
    "print(\"(mean:\", other['ethnicity_pct'].mean(),\", min:\",min(other['ethnicity_pct']),\", max:\",max(other['ethnicity_pct']),\")\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00bad50d",
   "metadata": {},
   "source": [
    "### Histogram of Event Sizes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "92f5465b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[\"eventregion\"] = data[\"newEventId\"].astype(str) + \"_\" + data[\"newRegionId\"].astype(str)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a08b1e04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(data['eventregion'].value_counts(), bins=10)  # arguments are passed to np.histogram\n",
    "plt.title(\"Histogram of Event Attendance\")\n",
    "plt.xlabel(\"Event Attendees\")\n",
    "plt.ylabel(\"Event Count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ef23570f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1_0     351\n",
       "0_1     349\n",
       "1_2     320\n",
       "6_0     304\n",
       "5_1     293\n",
       "       ... \n",
       "5_10     26\n",
       "2_26     25\n",
       "3_24     18\n",
       "2_25     16\n",
       "2_24     13\n",
       "Name: eventregion, Length: 141, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['eventregion'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1f02a1d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Range of event attendance values:\n",
      "\tSmallest event: 13\n",
      "\tLargest event: 351\n"
     ]
    }
   ],
   "source": [
    "print('Range of event attendance values:')\n",
    "print('\\tSmallest event:', min(data['eventregion'].value_counts()))\n",
    "print('\\tLargest event:', max(data['eventregion'].value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97202211",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
